OpenRaiser/Intern-Atlas huggingface dataset:
Modelling movement for collective adaptive systems with CARMA
Venue: arXiv cs.MA 2016
Year: 2016

Space and movement through space play an important role in many collective adaptive systems (CAS). CAS consist of multiple components interacting to achieve some goal in a system or environment that can change over time. When these components operate in space, then their behaviour can be affected by where they are located in that space. Examples include the possibility of communication between two components located at different points, and rates of movement of a component that may be affected by location. The CARMA language and its associated software tools can be used to model such systems. In particular, a graphical editor for CARMA allows for the specification of spatial structure and generation of templates that can be used in a CARMA model with space. We demonstrate the use of this tool to experiment with a model of pedestrian movement over a network of paths.
A Birthday Repetition Theorem and Complexity of Approximating Dense CSPs
Venue: arXiv cs.CC 2016
Year: 2016

A $(k \times l)$-birthday repetition $\mathcal{G}^{k \times l}$ of a two-prover game $\mathcal{G}$ is a game in which the two provers are sent random sets of questions from $\mathcal{G}$ of sizes $k$ and $l$ respectively. These two sets are sampled independently uniformly among all sets of questions of those particular sizes. We prove the following birthday repetition theorem: when $\mathcal{G}$ satisfies some mild conditions, $val(\mathcal{G}^{k \times l})$ decreases exponentially in $Ω(kl/n)$ where $n$ is the total number of questions. Our result positively resolves an open question posted by Aaronson, Impagliazzo and Moshkovitz (CCC 2014). As an application of our birthday repetition theorem, we obtain new fine-grained hardness of approximation results for dense CSPs. Specifically, we establish a tight trade-off between running time and approximation ratio for dense CSPs by showing conditional lower bounds, integrality gaps and approximation algorithms. In particular, for any sufficiently large $i$ and for every $k \geq 2$, we show the following results: - We exhibit an $O(q^{1/i})$-approximation algorithm for dense Max $k$-CSPs with alphabet size $q$ via $O_k(i)$-level of Sherali-Adams relaxation. - Through our birthday repetition theorem, we obtain an integrality gap of $q^{1/i}$ for $\tildeΩ_k(i)$-level Lasserre relaxation for fully-dense Max $k$-CSP. - Assuming that there is a constant $ε> 0$ such that Max 3SAT cannot be approximated to within $(1-ε)$ of the optimal in sub-exponential time, our birthday repetition theorem implies that any algorithm that approximates fully-dense Max $k$-CSP to within a $q^{1/i}$ factor takes $(nq)^{\tilde Ω_k(i)}$ time, almost tightly matching the algorithmic result based on Sherali-Adams relaxation.
Salient Region Detection and Segmentation in Images using Dynamic Mode Decomposition
Venue: arXiv cs.CV 2016
Year: 2016

Visual Saliency is the capability of vision system to select distinctive parts of scene and reduce the amount of visual data that need to be processed. The presentpaper introduces (1) a novel approach to detect salient regions by considering color and luminance based saliency scores using Dynamic Mode Decomposition (DMD), (2) a new interpretation to use DMD approach in static image processing. This approach integrates two data analysis methods: (1) Fourier Transform, (2) Principle Component Analysis.The key idea of our work is to create a color based saliency map. This is based on the observation thatsalient part of an image usually have distinct colors compared to the remaining portion of the image. We have exploited the power of different color spaces to model the complex and nonlinear behavior of human visual system to generate a color based saliency map. To further improve the effect of final saliency map, weutilized luminance information exploiting the fact that human eye is more sensitive towards brightness than color.The experimental results shows that our method based on DMD theory is effective in comparison with previous state-of-art saliency estimation approaches. The approach presented in this paperis evaluated using ROC curve, F-measure rate, Precision-Recall rate, AUC score etc.
Learning from the News: Predicting Entity Popularity on Twitter
Venue: arXiv cs.SI 2016
Year: 2016

In this work, we tackle the problem of predicting entity popularity on Twitter based on the news cycle. We apply a supervised learn- ing approach and extract four types of features: (i) signal, (ii) textual, (iii) sentiment and (iv) semantic, which we use to predict whether the popularity of a given entity will be high or low in the following hours. We run several experiments on six different entities in a dataset of over 150M tweets and 5M news and obtained F1 scores over 0.70. Error analysis indicates that news perform better on predicting entity popularity on Twitter when they are the primary information source of the event, in opposition to events such as live TV broadcasts, political debates or football matches.
Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity
Venue: arXiv cs.NE 2016
Year: 2016

Spike-timing-dependent plasticity (STDP) incurs both causal and acausal synaptic weight updates, for negative and positive time differences between pre-synaptic and post-synaptic spike events. For realizing such updates in neuromorphic hardware, current implementations either require forward and reverse lookup access to the synaptic connectivity table, or rely on memory-intensive architectures such as crossbar arrays. We present a novel method for realizing both causal and acausal weight updates using only forward lookup access of the synaptic connectivity table, permitting memory-efficient implementation. A simplified implementation in FPGA, using a single timer variable for each neuron, closely approximates exact STDP cumulative weight updates for neuron refractory periods greater than 10 ms, and reduces to exact STDP for refractory periods greater than the STDP time window. Compared to conventional crossbar implementation, the forward table-based implementation leads to substantial memory savings for sparsely connected networks supporting scalable neuromorphic systems with fully reconfigurable synaptic connectivity and plasticity.
Recurrent Memory Array Structures
Venue: arXiv cs.LG 2016
Year: 2016

The following report introduces ideas augmenting standard Long Short Term Memory (LSTM) architecture with multiple memory cells per hidden unit in order to improve its generalization capabilities. It considers both deterministic and stochastic variants of memory operation. It is shown that the nondeterministic Array-LSTM approach improves state-of-the-art performance on character level text prediction achieving 1.402 BPC on enwik8 dataset. Furthermore, this report estabilishes baseline neural-based results of 1.12 BPC and 1.19 BPC for enwik9 and enwik10 datasets respectively.
The SensorCloud Protocol: Securely Outsourcing Sensor Data to the Cloud
Venue: arXiv cs.NI 2016
Year: 2016

The increasing deployment of sensor networks, ranging from home networks to industrial automation, leads to a similarly growing demand for storing and processing the collected sensor data. To satisfy this demand, the most promising approach to date is the utilization of the dynamically scalable, on-demand resources made available via the cloud computing paradigm. However, prevalent security and privacy concerns are a huge obstacle for the outsourcing of sensor data to the cloud. Hence, sensor data needs to be secured properly before it can be outsourced to the cloud. When securing the outsourcing of sensor data to the cloud, one important challenge lies in the representation of sensor data and the choice of security measures applied to it. In this paper, we present the SensorCloud protocol, which enables the representation of sensor data and actuator commands using JSON as well as the encoding of the object security mechanisms applied to a given sensor data item. Notably, we solely utilize mechanisms that have been or currently are in the process of being standardized at the IETF to aid the wide applicability of our approach.
Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures
Venue: arXiv cs.NE 2016
Year: 2016

State-of-the-art neural networks are getting deeper and wider. While their performance increases with the increasing number of layers and neurons, it is crucial to design an efficient deep architecture in order to reduce computational and memory costs. Designing an efficient neural network, however, is labor intensive requiring many experiments, and fine-tunings. In this paper, we introduce network trimming which iteratively optimizes the network by pruning unimportant neurons based on analysis of their outputs on a large dataset. Our algorithm is inspired by an observation that the outputs of a significant portion of neurons in a large network are mostly zero, regardless of what inputs the network received. These zero activation neurons are redundant, and can be removed without affecting the overall accuracy of the network. After pruning the zero activation neurons, we retrain the network using the weights before pruning as initialization. We alternate the pruning and retraining to further reduce zero activations in a network. Our experiments on the LeNet and VGG-16 show that we can achieve high compression ratio of parameters without losing or even achieving higher accuracy than the original network.
Enhanced VIP Algorithms for Forwarding, Caching, and Congestion Control in Named Data Networks
Venue: arXiv cs.IT 2016
Year: 2016

Emerging Information-Centric Networking (ICN) architectures seek to optimally utilize both bandwidth and storage for efficient content distribution over the network. The Virtual Interest Packet (VIP) framework has been proposed to enable joint design of forwarding, caching, and congestion control strategies within the Named Data Networking (NDN) architecture. While the existing VIP algorithms exhibit good performance, they are primarily focused on maximizing network throughput and utility, and do not explicitly consider user delay. In this paper, we develop a new class of enhanced algorithms for joint dynamic forwarding, caching and congestion control within the VIP framework. These enhanced VIP algorithms adaptively stabilize the network and maximize network utility, while improving the delay performance by intelligently making use of VIP information beyond one hop. Generalizing Lyapunov drift techniques, we prove the throughput optimality and characterize the utility-delay tradeoff of the enhanced VIP algorithms. Numerical experiments demonstrate the superior performance of the resulting enhanced algorithms for handling Interest Packets and Data Packets within the actual plane, in terms of low network delay and high network utility.
Scalar Quadratic-Gaussian Soft Watermarking Games
Venue: arXiv cs.CR 2016
Year: 2016

We introduce the zero-sum game problem of soft watermarking: The hidden information (watermark) comes from a continuum and has a perceptual value; the receiver generates an estimate of the embedded watermark to minimize the expected estimation error (unlike the conventional watermarking schemes where both the hidden information and the receiver output are from a discrete finite set). Applications include embedding a multimedia content into another. We consider in this paper the scalar Gaussian case and use expected mean-squared distortion. We formulate the resulting problem as a zero-sum game between the encoder & receiver pair and the attacker. We show that for the lin- ear encoder, the optimal attacker is Gaussian-affine, derive the optimal system parameters in that case, and discuss the corresponding system behavior. We also provide numerical results to gain further insight and understanding of the system behavior at optimality.
Camera Elevation Estimation from a Single Mountain Landscape Photograph
Venue: arXiv cs.CV 2016
Year: 2016

This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment. We introduce a new benchmark dataset of one-hundred thousand images with annotated camera elevation called Alps100K. We propose and experimentally evaluate two automatic data-driven approaches to camera elevation estimation: one based on convolutional neural networks, the other on local features. To compare the proposed methods to human performance, an experiment with 100 subjects is conducted. The experimental results show that both proposed approaches outperform humans and that the best result is achieved by their combination.
An Introductory Course to Judgment Aggregation
Venue: arXiv cs.MA 2016
Year: 2016

Reaching some form of consensus is often necessary for autonomous agents that want to coordinate their actions or otherwise engage in joint activities. One way to reach a consensus is by aggregating individual information, such as decisions, beliefs, preferences and constraints. Judgment aggregation is a social choice method, which generalises voting, that studies the aggregation of individual judgments regarding the truth-value of logically related propositions. As such, judgment aggregation is applicable for consensus reaching problems in multi agent systems. As other social choice theory, judgment aggregation research is abundant with impossibility results. However, the aim of this tutorial is to give an introduction to the methods of judgment aggregation, not the impossibility results. In particular, the tutorial will introduce the basic frameworks that model judgment aggregation problems and give an overview of the judgment aggregation functions so far developed as well as their social theoretic and computational complexity properties. The focus of the tutorial are consensus reaching problems in multi agent systems that can be modelled as judgment aggregation problems. The desirable properties of a judgment aggregation method applied to these problems are not necessarily the same as properties desirable in legal or political contexts, which is considered to be the native domain of judgment aggregation. After this tutorial the participants are expected to be able to read and understand judgment aggregation literature and have a grasp on the state-of-the-art and open questions in judgment aggregation research of interest to multi agent systems.
Separating Answers from Queries for Neural Reading Comprehension
Venue: arXiv cs.CL 2016
Year: 2016

We present a novel neural architecture for answering queries, designed to optimally leverage explicit support in the form of query-answer memories. Our model is able to refine and update a given query while separately accumulating evidence for predicting the answer. Its architecture reflects this separation with dedicated embedding matrices and loosely connected information pathways (modules) for updating the query and accumulating evidence. This separation of responsibilities effectively decouples the search for query related support and the prediction of the answer. On recent benchmark datasets for reading comprehension, our model achieves state-of-the-art results. A qualitative analysis reveals that the model effectively accumulates weighted evidence from the query and over multiple support retrieval cycles which results in a robust answer prediction.
Novel 16-QAM and 64-QAM Near-Complementary Sequences with Low PMEPR in OFDM Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we firstly propose a novel construction of $16$-quadrature amplitude modulation (QAM) near-complementary sequences with low peak-to-mean envelope power ratio (PMEPR) in orthogonal frequency division multiplexing (OFDM) systems. The proposed $16$-QAM near-complementary sequences can be constructed by utilizing novel nonlinear offsets, where the length of the sequences is $n=2^m$. The family size of the newly constructed $16$-QAM near-complementary sequences is $8\times (\frac{m!}{2})\times 4^{m+1}$, and the PMEPR of these sequences is proven to satisfy ${\textrm{PMEPR}}\leq 2.4$. Thus, the proposed construction can generate a number of $16$-QAM near-complementary sequences with low PMEPR, resulting in the improvement of the code rate in OFDM systems. Furthermore, we also propose a novel construction of $64$-QAM near-complementary sequences with low PMEPR, which is the first proven construction of $64$-QAM near-complementary sequences. The PMEPRs of two types of the proposed $64$-QAM near-complementary sequences are proven to satisfy that ${\textrm{PMEPR}}\leq 3.62$ or ${\textrm{PMEPR}}\leq 2.48$, respectively. The family size of the newly constructed $64$-QAM near-complementary sequences is $64\times (\frac{m!}{2})\times 4^{m+1}$.
Performance Optimization of WSNs using External Information
Venue: arXiv cs.NI 2016
Year: 2016

The goal of this work is to describe a self-management system that correlates data sensed by different Wireless Sensor Networks (WSNs) and adjusts the number of active nodes in each network to provide an appropriate amount of measurements. The architecture considers the factors that make the external data relevant to the local network, such as the distance between covered areas, the relation between the types of sensed data and the reliability of the measurements. As a result, the operation of each network will be tuned to trade-off the accuracy of the measurements and the power consumption.
Pseudo-Centroid Clustering
Venue: arXiv cs.DS 2016
Year: 2016

Pseudo-Centroid Clustering replaces the traditional concept of a centroid expressed as a center of gravity with the notion of a pseudo-centroid (or a coordinate free centroid) which has the advantage of applying to clustering problems where points do not have numerical coordinates (or categorical coordinates that are translated into numerical form). Such problems, for which classical centroids do not exist, are particularly important in social sciences, marketing, psychology and economics, where distances are not computed from vector coordinates but rather are expressed in terms of characteristics such as affinity relationships, psychological preferences, advertising responses, polling data, market interactions and so forth, where distances, broadly conceived, measure the similarity (or dissimilarity) of characteristics, functions or structures. We formulate a K-PC algorithm analogous to a K-Means algorithm, and identify two key types of pseudo-centroids, MinMax centroids and (weighted) MinSum centroids, and describe how they respectively give rise to a K-MinMax algorithm and a K-MinSum algorithm which are analogous to a K-Means algorithm. The K-PC algorithms are able to take advantage of problem structure to identify special diversity-based and intensity-based starting methods to generate initial pseudo-centroids and associated clusters, accompanied by theorems for the intensity-based methods that establish their ability to obtain best clusters of a selected size from the points available at each stage of construction. We also introduce a Regret-Threshold PC algorithm that modifies the K-PC algorithm together with an associated diversification method and a new criterion for evaluating the quality of a collection of clusters.
IntelliAd Understanding In-APP Ad Costs From Users Perspective
Venue: arXiv cs.CY 2016
Year: 2016

Ads are an important revenue source for mobile app development, especially for free apps, whose expense can be compensated by ad revenue. The ad benefits also carry with costs. For example, too many ads can interfere the user experience, leading to less user retention and reduced earnings ultimately. In the paper, we aim at understanding the ad costs from users perspective. We utilize app reviews, which are widely recognized as expressions of user perceptions, to identify the ad costs concerned by users. Four types of ad costs, i.e., number of ads, memory/CPU overhead, traffic usage, and bettery consumption, have been discovered from user reviews. To verify whether different ad integration schemes generate different ad costs, we first obtain the commonly used ad schemes from 104 popular apps, and then design a framework named IntelliAd to automatically measure the ad costs of each scheme. To demonstrate whether these costs indeed influence users reactions, we finally observe the correlations between the measured ad costs and the user perceptions. We discover that the costs related to memory/CPU overhead and battery consumption are more concerned by users, while the traffic usage is less concerned by users in spite of its obvious variations among different schemes in the experiments. Our experimental results provide the developers with suggestions on better incorporating ads into apps and, meanwhile, ensuring the user experience.
An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling
Venue: arXiv cs.HC 2016
Year: 2016

A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. For example, EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm design in current ERP-based letter by letter typing BCIs typically query the user with an arbitrary subset characters. However, the typing accuracy and also typing speed can potentially be enhanced with more informed subset selection and flash assignment. In this manuscript, we introduce the active recursive Bayesian state estimation (active-RBSE) framework for inference and sequence optimization. Prior to presentation in each iteration, rather than showing a subset of randomly selected characters, the developed framework optimally selects a subset based on a query function. Selected queries are made adaptively specialized for users during each intent detection. Through a simulation-based study, we assess the effect of active-RBSE on the performance of a language-model assisted typing BCI in terms of typing speed and accuracy. To provide a baseline for comparison, we also utilize standard presentation paradigms namely, row and column matrix presentation paradigm and also random rapid serial visual presentation paradigms. The results show that utilization of active-RBSE can enhance the online performance of the system, both in terms of typing accuracy and speed.
Cloud Empowered Self-Managing WSNs
Venue: arXiv cs.NI 2016
Year: 2016

Wireless Sensor Networks (WSNs) are composed of low powered and resource-constrained wireless sensor nodes that are not capable of performing high-complexity algorithms. Integrating these networks into the Internet of Things (IoT) facilitates their real-time optimization based on remote data visualization and analysis. This work describes the design and implementation of a scalable system architecture that integrates WSNs and cloud services to work autonomously in an IoT environment. The implementation relies on Software Defined Networking features to simplify the WSN management and exploits data analytics tools to execute a reinforcement learning algorithm that takes decisions based on the environment's evolution. It can automatically configure wireless sensor nodes to measure and transmit the temperature only at periods when the environment changes more often. Without any human intervention, the system could reduce nearly 85% the number of transmissions, showing the potential of this mechanism to extend WSNs lifetime without compromising the data quality. Besides attending to similar use cases, such a WSN autonomic management could promote a new business model to offer sensing tasks as a service, which is also introduced in this work.